Highly Disaggregated Land Unavailability

Chandler Lutz, Ben Sand

Abstract

Using high resolution satellite imagery data and GIS software, we compute the percentage of undevelopable land -- Land Unavailability -- at levels high levels of geographic disaggregation down to the zip code level. Our Land Unavailability measure expands on the popular proxy from Saiz (2010) by (1) using higher resolution satellite imagery from the USGS; (2) more accurate geographic boundaries; and (3) multiple levels of disaggregation. First, we document the importance of using precise boundary files and disaggregated data in the construction of land unavailability. Less precise boundary files lead to measurement error in land unavailability that can violate standard instrumental variable assumptions, while larger aggregated areas (e.g. MSAs in California and the Southwest) have larger variance in Land Unavailability and thus yield less precise two-stage least squares estimates. Next using data at the zip code level with nearly complete coverage of the contiguous US we show that Land Unavailability is uncorrelated with housing demand proxies, validating Land Unavailability as an instrument for house prices. Further we find that within local housing markets (e.g. after controlling for aggregated Land Unavailability) that higher Land Unavailability is associated with lower house price growth but higher absolute prices, congruent with inter-market mobility and financially constrained households substituting expensive for cheap housing during a boom.